Event-based image and video reconstruction.   Converting event streams (binary) to multiple-valued intensity frames is a challenging task, yet has been shown beneficial to downstream visual tasks [1]}. Existing strategies for image reconstruction include dictionary learning [2]}, manifold regularization [3]}, optical flow [4]}, exponential integration [5]}, [6]}, conditional Generative Adversarial Networks (GAN) [7]} and recurrent neural network [1]}. Compared to existing algorithms, our work is the first, to the best of our knowledge, to unify different temporal frame synthesis settings, including interpolation, extrapolation (prediction) and motion deblur (reconstructing a video from a motion-blurred image).
